Effects of Causes and Causes of Effects

TL;DR

This paper distinguishes two key areas in statistical causality—effects of causes and causes of effects—comparing formal frameworks and discussing the role of counterfactuals in each.

Contribution

It clarifies the conceptual differences between effects of causes and causes of effects, and evaluates various formal models used for causal inference.

Findings

Counterfactuals are unnecessary for effects of causes.

Counterfactuals are essential but arbitrary for causes of effects.

Additional structure reduces but does not eliminate counterfactual arbitrariness.

Abstract

We describe and contrast two distinct problem areas for statistical causality: studying the likely effects of an intervention ("effects of causes"), and studying whether there is a causal link between the observed exposure and outcome in an individual case ("causes of effects"). For each of these, we introduce and compare various formal frameworks that have been proposed for that purpose, including the decision-theoretic approach, structural equations, structural and stochastic causal models, and potential outcomes. It is argued that counterfactual concepts are unnecessary for studying effects of causes, but are needed for analysing causes of effects. They are however subject to a degree of arbitrariness, which can be reduced, though not in general eliminated, by taking account of additional structure in the problem.